Insurers can now attribute accurate wildfire risk ratings during the
underwriting process to any commercial or private location in the United
States. Underwriting calculations can now integrate wildfire risk in
much the same way they account for flood risk to establish correct
policy pricing.

The data suite supports cat modeling with fire behavior science provided
by
Anchor
Point Group Fire Management Consultants. The vast array of
vegetation across the U.S., weather history and key geological
attributes are calculated by location to help predict how wildfires are
fueled and how they might spread.

“For the first time, insurers have the national geospatial data for
underwriting wildfire risk across the U.S.,” said John O’Hara, executive
vice president and president, Pitney Bowes Software. “We are excited to
deliver critical fire science intelligence that will help insurers to
more accurately identify, assess and mitigate risk exposure from these
catastrophic events.”

With Wildfire Risk Data, hundreds of millions of policies may be
processed in an hour. It is available as software-as-a-service or as an
on premise solution. Insurers can use spatial data to assess wildfire
risk at a particular location without the need to visualize the data
with each location on a map. For more information on Pitney Bowes
Wildfire Risk Data, contact Bill Sinn at 215-368-1556 or
Email Contact.

About the RAA’s Cat Modeling 2012 Conference, February 14-16, 2012 in
Orlando, Florida

Catastrophe models are a key risk management tool for insurers and
reinsurers. Regulators, insurers, rating agencies, and financial
institutions use model output in their analysis and evaluation of
enterprise risk. Cat Models—The New Risk addresses
the challenge of balancing the need for stable risk assessment tools
versus the need for continued model improvement as better scientific,
building performance, and historical loss data becomes available. The
Conference reviews these issues in order to provide various means for
improving the interpretation of model output and methods for making
decisions when data or analytics are less accessible.